Our ability to collect data far outpaces our ability tofully utilize it—yet those data may hold the key tosolving some of the biggest global challenges facingus today.
我们搜集信息的能力远远强于分析使用的能力，然而，这些消息可能包含了我们现如今正在面临的全球性挑战的解决办法。
Take, for instance, the frequent outbreaks ofwaterborne illnesses as a consequence of war ornatural disasters. The most recent example can befound in Yemen, where roughly 10,000 newsuspected cases of cholera are reported each week—and history is riddled with similar stories. What if we could better understand the environmental factors that contributed to the disease, predict which communities are at higher risk, and put in place protective measures to stemthe spread?
比如，战后或自然灾难引起的水源性传播疾病频繁爆发。最近的例子发生在也门，每个星期也门新发现约一万例疑似霍乱病例。而且历史总是相似的。如果我们能更好地理解环境因素对该病的影响，提前预测高风险社区，以保护性方法来阻止源头传播，将会怎么样呢？
Answers to these questions and others like them could potentially help us avert catastrophe.
这些问题和其他相似问题的答案可能会潜在地帮助我们阻止灾难。
We already collect data related to virtually everything, from birth and death rates to cropyields and traffic flows. IBM estimates that each day, 2.5 quintillion bytes of data aregenerated. To put that in perspective: that's the equivalent of all the data in the Library ofCongress being produced more than 166,000 times per 24-hour period. Yet we don't reallyharness the power of all this information. It's time that changed—and thanks to recentadvances in data analytics and computational services, we finally have the tools to do it.
我们几乎为每样东西收集数据，从出生率死亡率到粮食变量和交通状况。IBM公司估计每天有2.5个五万亿字节的数据产生。从这个角度来看：这等同于美国国会图书馆每24小时产生的数据的16.6万倍。但我们并不能掌控所有的信息。但由于近来先进的数据分析和计算机服务，我们终于有了改变它的工具。
As a data scientist for Los Alamos National Laboratory, I study data from wide-ranging, publicsources to identify patterns in hopes of being able to predict trends that could be a threat toglobal security. Multiple data streams are critical because the ground-truth data (such assurveys) that we collect is often delayed, biased, sparse, incorrect or, sometimes, nonexistent.
作为洛斯阿拉莫斯国家实验室的数据科学家，我研究来自广泛公共来源的数据，以确定模式，希望能够预测可能对全球安全构成威胁的趋势。多个数据流是至关重要的，因为我们收集的基本事实数据(比如调查)常常是延迟的、有偏见的、稀疏的、不正确的，有时甚至是不存在的。
For example, knowing mosquito incidence in communities would help us predict the risk ofmosquito-transmitted disease such as dengue, the leading cause of illness and death in thetropics. However, mosquito data at a global (and even national) scale are not available.
举个例子，了解蚊子在一个社区的叮咬发生率将会帮助我们预测蚊子的传染登革热病的风险，登革热是导致热带地区疾病和死亡的首要原因。然而，目前还没有全球(甚至全国)规模的蚊虫数据。
To address this gap, we're using other sources such as satellite imagery, climate data anddemographic information to estimate dengue risk. Specifically, we had success predicting thespread of dengue in Brazil at the regional, state and municipality level using these datastreams as well as clinical surveillance data and Google search queries that used termsrelated to the disease. While our predictions aren't perfect, they show promise. Our goal is tocombine information from each data stream to further refine our models and improve theirpredictive power.
为了弥补这一差距，我们正在利用卫星图像、气候数据和人口信息等其他来源来估计登革热风险。具体来说，我们成功地利用这些数据流、临床监测数据和使用与疾病有关的术语的谷歌搜索查询，预测了登革热在巴西的地区、州和市一级的蔓延。虽然我们的预测并不完美，但它们显示出了希望。我们的目标是将来自每个数据流的信息结合起来，以进一步完善我们的模型并提高它们的预测能力。
Similarly, to forecast the flu season, we have found that Wikipedia and Google searches cancomplement clinical data. Because the rate of people searching the internet for flu symptomsoften increases during their onset, we can predict a spike in cases where clinical data lags.
同样，为了预测流感季节，我们发现维基百科和谷歌搜索可以补充临床数据。由于人们在互联网上搜索流感症状的比率在发病期间经常增加，我们可以预测到临床数据滞后的病例会出现激增。
We're using these same concepts to expand our research beyond disease prediction to betterunderstand public sentiment. In partnership with the University of California, we'reconducting a three-year study using disparate data streams to understand whether opinionsexpressed on social media map to opinions expressed in surveys.
我们用同样的概念来扩展我们的研究以更好地理解大众的想法。我们正在进行一项与加州大学合作的为期三年的研究，该研究运用不同的数据流来了解社交媒体上所表达的观点是否与调查中所表述的一致。
For example, in Colombia, we are conducting a study to see whether social media posts aboutthe peace process between the government and FARC, the socialist guerilla movement, can beground-truthed with survey data. A University of California, Berkeley researcher is conductingon-the-ground surveys throughout Colombia—including in isolated rural areas—to pollcitizens about the peace process. Meanwhile, at Los Alamos, we're analyzing social media dataand news sources from the same areas to determine if they align with the survey data.
例如，在哥伦比亚，我们正在进行一项研究，看看关于政府和社会主义游击队运动之间和平进程的社交媒体帖子是否可以用调查数据来证实。加州大学伯克利分校的一名研究员正在哥伦比亚各地(包括偏远的农村地区)进行实地调查，调查公民对和平进程的看法。与此同时，在洛斯阿拉莫斯，我们正在分析来自同一地区的社交媒体数据和新闻来源，以确定它们是否与调查数据一致。
If we can demonstrate that social media accurately captures a population's sentiment, itcould be a more affordable, accessible and timely alternative to what are otherwiseexpensive and logistically challenging surveys. In the case of disease forecasting, if socialmedia posts did indeed serve as a predictive tool for outbreaks, those data could be used ineducational campaigns to inform citizens of the risk of an outbreak (due to vaccineexemptions, for example) and ultimately reduce that risk by promoting protective behaviors(such as washing hands, wearing masks, remaining indoors, etc. ).
如果我们能证明社交媒体能准确捕捉公众情绪，相较于昂贵、交通十分不便的调查而言，它就可以成为一种更实惠、可获取和及时的替代方法。如预测疾病时，如果社交媒体数据确实是有效预测疾病爆发的工具，这些数据就可以用来教育公众，告诉他们有疾病爆发的风险（例如疫苗豁免），并最终通过促进保护性措施来减小危害（如吸收、戴口罩、待在室内等）。
All of this illustrates the potential for big data to solve big problems. Los Alamos and othernational laboratories that are home to some of the world's largest supercomputers have thecomputational power augmented by machine learning and data analysis to take thisinformation and shape it into a story that tells us not only about one state or even nation, butthe world as a whole. The information is there; now it's time to use it.
所有这些都表明用大数据解决大问题的潜力。洛斯阿拉莫斯和其他国家实验室拥有世界最大的超级电脑，且因为机器学习和数据分析，其运算能力更加强大，因此可以运用信息，传递消息，不仅仅惠及一个州，一个国家，而且是整个世界。信息就在那里，是时候使用它了。
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